{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"/Users/andre/Downloads/water.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>location</th>\n",
       "      <th>town</th>\n",
       "      <th>mortality</th>\n",
       "      <th>hardness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>South</td>\n",
       "      <td>Bath</td>\n",
       "      <td>1247</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>North</td>\n",
       "      <td>Birkenhead</td>\n",
       "      <td>1668</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>South</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>1466</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>North</td>\n",
       "      <td>Blackburn</td>\n",
       "      <td>1800</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>North</td>\n",
       "      <td>Blackpool</td>\n",
       "      <td>1609</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>South</td>\n",
       "      <td>Walsall</td>\n",
       "      <td>1527</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58</td>\n",
       "      <td>South</td>\n",
       "      <td>West Bromwich</td>\n",
       "      <td>1627</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>South</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>1486</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>South</td>\n",
       "      <td>Wolverhampton</td>\n",
       "      <td>1485</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61</td>\n",
       "      <td>North</td>\n",
       "      <td>York</td>\n",
       "      <td>1378</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0 location           town  mortality  hardness\n",
       "0            1    South           Bath       1247       105\n",
       "1            2    North     Birkenhead       1668        17\n",
       "2            3    South     Birmingham       1466         5\n",
       "3            4    North      Blackburn       1800        14\n",
       "4            5    North      Blackpool       1609        18\n",
       "..         ...      ...            ...        ...       ...\n",
       "56          57    South        Walsall       1527        60\n",
       "57          58    South  West Bromwich       1627        53\n",
       "58          59    South       West Ham       1486       122\n",
       "59          60    South  Wolverhampton       1485        81\n",
       "60          61    North           York       1378        71\n",
       "\n",
       "[61 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_correaltion(x, y):\n",
    "    plt.scatter(x, y)\n",
    "    statistic_spearman, p_spearmen = stats.spearmanr(x, y)\n",
    "    statistic_pearson, p_pearson = stats.pearsonr(x, y)\n",
    "    if p_spearmen < 0.15:\n",
    "        print(f\"Корреляция Спирмена имеет статистическую значимость ( p < 0.15) и равна {str(statistic_spearman)}\")\n",
    "    elif p_spearmen > 0.15:\n",
    "        print(f\"Корреляция Спирмена не имеет статистической значимости ( p > 0.15) и равна {str(statistic_spearman)}\")\n",
    "\n",
    "    if p_pearson < 0.15:\n",
    "        print(f\"Корреляция Пирсона имеет статистическую значимость ( p < 0.15) и равна {str(statistic_pearson)}\")\n",
    "    elif p_pearson > 0.15:\n",
    "        print(f\"Корреляция Пирсона не имеет статистической значимости ( p > 0.15) и равна {str(statistic_pearson)}\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task #1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Корреляция Спирмена имеет статистическую значимость ( p < 0.15) и равна -0.6316646189166502\n",
      "Корреляция Пирсона имеет статистическую значимость ( p < 0.15) и равна -0.6548486232042466\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjEAAAGdCAYAAADjWSL8AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA2sklEQVR4nO3de3TU9Z3/8dfkQoIsGUloMokEGl0XiaFYUSCUFZFbXEN0+9v1Qo3sWQ+0VEAUKNKuB9nditit9kJB9HjKWaOle84CQtsdDSuC/EiIJkaNoChNKZfEWAgTgiTEzPf3B78ZmVzITDK37/f7fJzDOWbmk/CdD3G+r/l83p/Px2EYhiEAAACTSYj1BQAAAPQHIQYAAJgSIQYAAJgSIQYAAJgSIQYAAJgSIQYAAJgSIQYAAJgSIQYAAJhSUqwvIFK8Xq9OnjypoUOHyuFwxPpyAABAEAzD0NmzZ5WTk6OEhMuPtVg2xJw8eVK5ubmxvgwAANAPx44d04gRIy7bxrIhZujQoZIudkJaWlqMrwYAAASjpaVFubm5/vv45Vg2xPimkNLS0ggxAACYTDClIBT2AgAAUyLEAAAAUyLEAAAAUyLEAAAAUwopxKxdu1Y333yzhg4dqszMTN111136+OOPA9oYhqEnnnhCOTk5Gjx4sG699VZ9+OGHAW3a29u1ePFiDR8+XEOGDFFJSYmOHz8e0Ka5uVmlpaVyOp1yOp0qLS3VmTNn+vcqAQCA5YQUYvbs2aOHHnpIlZWVKi8v15dffqlZs2bp3Llz/jZPP/20nnnmGa1fv15vv/22XC6XZs6cqbNnz/rbLF26VNu2bdOWLVu0b98+tba2qri4WJ2dnf42c+fOVW1trdxut9xut2pra1VaWhqGlwwAACzBGICmpiZDkrFnzx7DMAzD6/UaLpfLeOqpp/xt2traDKfTaTz33HOGYRjGmTNnjOTkZGPLli3+NidOnDASEhIMt9ttGIZhHDx40JBkVFZW+ttUVFQYkoyPPvooqGvzeDyGJMPj8QzkJQIAgCgK5f49oJoYj8cjSUpPT5ck1dfXq7GxUbNmzfK3SUlJ0dSpU7V//35JUnV1tTo6OgLa5OTkqKCgwN+moqJCTqdTEydO9LeZNGmSnE6nvw0AALC3fm92ZxiGHn30UU2ZMkUFBQWSpMbGRklSVlZWQNusrCwdPXrU32bQoEEaNmxYtza+729sbFRmZma3vzMzM9Pfpqv29na1t7f7v25paennKwuPTq+hqvrTajrbpsyhqZqQl67EBM5wAgAgXPodYhYtWqT3339f+/bt6/Zc1132DMPoc+e9rm16an+5n7N27VqtWbMmmEuPOHddg9bsPKgGT5v/sWxnqlbPyVdRQXYMrwwAAOvo13TS4sWLtWPHDu3evTvgcCaXyyVJ3UZLmpqa/KMzLpdLFy5cUHNz82XbfPbZZ93+3s8//7zbKI/PqlWr5PF4/H+OHTvWn5c2YO66Bi0sqwkIMJLU6GnTwrIauesaYnJdAABYTUghxjAMLVq0SFu3btUbb7yhvLy8gOfz8vLkcrlUXl7uf+zChQvas2ePJk+eLEkaP368kpOTA9o0NDSorq7O36awsFAej0dVVVX+NgcOHJDH4/G36SolJcV/TlKszkvq9Bpas/OgjB6e8z22ZudBdXp7agEAAEIR0nTSQw89pFdeeUWvvvqqhg4d6h9xcTqdGjx4sBwOh5YuXaonn3xS1157ra699lo9+eSTuuKKKzR37lx/2wcffFDLli1TRkaG0tPTtXz5co0dO1YzZsyQJI0ZM0ZFRUWaP3++Nm3aJElasGCBiouLNXr06HC+/rCqqj/dbQTmUoakBk+bqupPq/CajOhdGAAAFhRSiNm4caMk6dZbbw14/Ne//rX+6Z/+SZL0gx/8QOfPn9f3v/99NTc3a+LEiXr99dcDjtR+9tlnlZSUpLvvvlvnz5/X9OnTtXnzZiUmJvrbvPzyy1qyZIl/FVNJSYnWr1/fn9cYNU1new8w/WkHAAB65zAMw5JzGy0tLXI6nfJ4PFGbWqo4ckr3vVDZZ7vfzJ/ESAwAAD0I5f7N2UlhNCEvXdnOVPW2Dsuhi6uUJuSlR/OyAACwJEJMGCUmOLR6Tr4kdQsyvq9Xz8lnvxgAAMKAEBNmRQXZ2nj/jXI5UwMedzlTtfH+G9knBgCAMOn3ZnfoXVFBtmbmu9ixFwCACCLEREhigoPiXQAAIojpJAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEpJsb4Aq+r0GqqqP62ms23KHJqqCXnpSkxwxPqyAACwDEJMBLjrGrRm50E1eNr8j2U7U7V6Tr6KCrJjeGUAAFgH00lh5q5r0MKymoAAI0mNnjYtLKuRu64hRlcGAIC1EGLCqNNraM3OgzJ6eM732JqdB9Xp7akFAAAIBSEmjKrqT3cbgbmUIanB06aq+tPRuygAACyKEBNGTWd7DzD9aQcAAHpHiAmjzKGpYW0HAAB6R4gJowl56cp2pqq3hdQOXVylNCEvPZqXBQCAJRFiwigxwaHVc/IlqVuQ8X29ek5+WPaL6fQaqjhySq/WnlDFkVMUCwMAbId9YsKsqCBbG++/sds+Ma4w7hPDPjQAAEgOwzAs+RG+paVFTqdTHo9HaWlpUf/7I7Vjr28fmq7/aL6fvPH+GwkyAADTCuX+zUhMhCQmOFR4TUbYfl6n11DlkVN67L8/6HUfGocu7kMzM9/FEQcAAMsLuSZm7969mjNnjnJycuRwOLR9+/aA51tbW7Vo0SKNGDFCgwcP1pgxY7Rx48aANu3t7Vq8eLGGDx+uIUOGqKSkRMePHw9o09zcrNLSUjmdTjmdTpWWlurMmTMhv0ArcNc1aMq6N/SdFw/ozPmOXtuxDw0AwE5CDjHnzp3TuHHjtH79+h6ff+SRR+R2u1VWVqZDhw7pkUce0eLFi/Xqq6/62yxdulTbtm3Tli1btG/fPrW2tqq4uFidnZ3+NnPnzlVtba3cbrfcbrdqa2tVWlraj5dobr0dY3A57EMDALCDkKeTbr/9dt1+++29Pl9RUaF58+bp1ltvlSQtWLBAmzZt0jvvvKM777xTHo9HL774ol566SXNmDFDklRWVqbc3Fzt2rVLs2fP1qFDh+R2u1VZWamJEydKkl544QUVFhbq448/1ujRo/vxUs3ncscYXA770AAA7CDsS6ynTJmiHTt26MSJEzIMQ7t379bhw4c1e/ZsSVJ1dbU6Ojo0a9Ys//fk5OSooKBA+/fvl3QxCDmdTn+AkaRJkybJ6XT623TV3t6ulpaWgD9m19cxBl2xDw0AwE7CHmJ+8YtfKD8/XyNGjNCgQYNUVFSkDRs2aMqUKZKkxsZGDRo0SMOGDQv4vqysLDU2NvrbZGZmdvvZmZmZ/jZdrV271l8/43Q6lZubG+ZXFn2hTAuFex8aAADiXURCTGVlpXbs2KHq6mr99Kc/1fe//33t2rXrst9nGIYcjq9uvpf+d29tLrVq1Sp5PB7/n2PHjg3shcSBUKaFXM5UllcDAGwlrEusz58/rx/+8Ifatm2b7rjjDknSN77xDdXW1uo//uM/NGPGDLlcLl24cEHNzc0BozFNTU2aPHmyJMnlcumzzz7r9vM///xzZWVl9fh3p6SkKCUlJZwvJ+Z8xxg0etp6rYu5cnCyfvWdGzXp6gxGYAAAthLWkZiOjg51dHQoISHwxyYmJsrr9UqSxo8fr+TkZJWXl/ufb2hoUF1dnT/EFBYWyuPxqKqqyt/mwIED8ng8/jZ20NcxBg5JT/2fsfrWXw8nwAAAbCfkkZjW1lZ9+umn/q/r6+tVW1ur9PR0jRw5UlOnTtWKFSs0ePBgjRo1Snv27NF//ud/6plnnpEkOZ1OPfjgg1q2bJkyMjKUnp6u5cuXa+zYsf7VSmPGjFFRUZHmz5+vTZs2Sbq4yqm4uNg2K5N8onGMAQAAZhTysQNvvvmmpk2b1u3xefPmafPmzWpsbNSqVav0+uuv6/Tp0xo1apQWLFigRx55xF/P0tbWphUrVuiVV17R+fPnNX36dG3YsCGgGPf06dNasmSJduzYIUkqKSnR+vXrdeWVVwZ1nbE+diDcInWMAQAA8SSU+zdnJ5kUoQYAYEWcnWRxnGINAEAEllgjsno7hqDR06aFZTVy1zXE6MoAAIguQoyJXO4YAt9ja3YeVKfXkjOEAAAEIMSYSF/HEHCKNQDATggxJhLsMQScYg0AsAMKe+NIXyuOgj2GgFOsAQB2QIiJE8GsOOrrGAKHLm6CxynWAAA7YDopDgS74qivYwgkTrEGANgHISbGQl1x5DuGwOUMnDLiFGsAgN0wnRRjoaw4KrwmQ9LFIDMz38WOvQAAWyPEREiwxwL0d8VRYoLDH2oAALAjQkwEhHIsACuOAADoH2piwizUYwF8K456mwhy6GIAYsURAACBCDFh1J9jAVhxBABA/xBiwqi/xwKw4ggAgNBRExNGAzkWgBVHAACEhhATRgMt0mXFEQAAwSPEDEDXZdTjRw3jWACbCXYpPQAg/Agx/dTbMuqScdl6fm+9HFJAkKFI13pCWUoPAAg/Cnv74XLLqJ/fW68Ft+RRpGtxoS6lBwCEHyMxIeprGbVD0o73GrRnxTRVH21mmsGCgvkdWLPzoGbmu/g3B4AIIsSEKNhl1NVHmynStaj+nHeF8KAGCcClCDEhGsgy6lDxhh2fovk7gK+46xr0xI4P1djS7n/MlZaiJ0quZ5oWsClCTIiiddYRRaPxi/Ouos9d16DvldV0e7yxpV3fK6vRc9SbAbZEYW+IonHWkVmLRju9hiqOnNKrtSdUceRUwPEKVsJ5V9HV6TX02NYPLtvmsa0fWPb3DUDvCDEhivRZR/05fykeuOsaNGXdG7rvhUo9vKVW971QqSnr3ojbwDUQl/4O9Ial9OFTeeSUznzRcdk2Z77oUOWRU1G6IgDxghDTD5E866i/5y/FkllHjgaiqCBbC27JU9eckuCQFtySx9RGGFX88S9hbQfAOqiJ6aeigmzddl2WXqr4k46e/kKj0q9QaeHXNShpYLnQbEWjdl1u7K5r0PN767u9bsOQnt9br2+OHEaQCZtgf2+s8/sFIDiMxPSTu65BU3+yW//2+0P6z4qj+rffH9LUn+we8KiD2YpGzThyNFBmnfIzq2CXqbOcHbAfQkw/RHL6xGxFo2YbOQoHOwa3WJp0dYauvCL5sm2GXZGsSVcTYgC7IcSEKNKfwiNdOBxuZhs5Cgc7BrdYSkxw6Klvj71sm7XfHhs3/08AiB5CTIii8Sk8koXD4Wa2kaNwsGNwi7Wigmw9d/+NcqUF9mm2M5U9YgAbo7A3RNH6FF5UkK2Z+a6437HXN3K0sKzGNid3+4Jbo6etxxE5hy4GTisFt3hglv8nAEQPISZE0fwUnpjgMEWxom/kqOsOwy6L7jBsx+AWL8zy/wSA6CDEhIhP4T2z26dkuwU3AIhHDsMwLLkOtKWlRU6nUx6PR2lpaWH92b7VSVLPn8LjrW4FkcMhnQAQXqHcvwkx/cQBjQAAhF8o92+mk/rJbtMnAADEG0LMAMRjkSHTGwAAuyDEWAhTXAAAO2GzO4uw40nSAAB7I8RYAAcSAgDsiBBjARxICACwI0KMBXAgIQDAjggxFsCBhAAAOyLEWIAdT5IGAIAQYwG+AwkldQsyHEgIALAqQoxF+A4kdDkDp4xczlTOcgIAWBKb3VkIRyGgL+zoDMBKCDExEMkbSTwehYD4wI7OAKyGEBNl3EgQC74dnbtud+jb0ZkpRwBmRE1MFHE0AGKBHZ0BWBUhJkq4kSBW2NEZgFURYqKEGwnCrdNrqOLIKb1ae0IVR071GoDZ0RmAVVETEyX9uZFEeiUJK1XMK5TaKnZ0BmBVhJgoCfVGEukCYAqMzSvUIl3fjs6NnrYepzMdurifEDs6AzAbppOiJJSjASJdAEyBsXn1p7aKHZ0BWBUhJkqCvZFIimgBMAXG5tbf2ip2dAZgRUwnRZHvRtJ1Gsd1yTROxZFTQd+k+rOpXSg3QTbNiz8DKdJlR2cAVkOIibK+biSRXknCShVzG2iRLjs6A7ASQkwMXO5GEu6VJF1XIA3/q5Sw/nxEF0W6APAVQkycCedNqqcVSK60FF15RbI8X3RwEzQhX23VwrIaOaSAf0OKdAHYDYW9cSZcK0l6W4H0WUu7zvz/AMNKFXOiSBcALnIYhmHJZSgtLS1yOp3yeDxKS0uL9eWEbCD7uHR6DU1Z90avBbwOSc4rkpWalKjGFvaJMSs2KwRgRaHcv0OeTtq7d69+8pOfqLq6Wg0NDdq2bZvuuuuugDaHDh3SypUrtWfPHnm9Xl1//fX6r//6L40cOVKS1N7eruXLl+s3v/mNzp8/r+nTp2vDhg0aMWKE/2c0NzdryZIl2rFjhySppKREv/zlL3XllVeGesmmNJCVJMGsQDrzRYdefvBGJSQ4uAmaFEW6AOwu5Omkc+fOady4cVq/fn2Pzx85ckRTpkzRddddpzfffFPvvfeeHn/8caWmfjX0vXTpUm3btk1btmzRvn371NraquLiYnV2dvrbzJ07V7W1tXK73XK73aqtrVVpaWk/XqJ5+W5Sd95wlQqvyQg6YAS7sugv59r79fMBAIgHA5pOcjgc3UZi7r33XiUnJ+ull17q8Xs8Ho++9rWv6aWXXtI999wjSTp58qRyc3P1hz/8QbNnz9ahQ4eUn5+vyspKTZw4UZJUWVmpwsJCffTRRxo9enSf12b26aSBqDhySve9UNlnu9/Mn8QneQBAXAnl/h3Wwl6v16vf//73+pu/+RvNnj1bmZmZmjhxorZv3+5vU11drY6ODs2aNcv/WE5OjgoKCrR//35JUkVFhZxOpz/ASNKkSZPkdDr9bbpqb29XS0tLwB+76Hqa8fhRw4I+4gCxF+xp1ACAQGFdYt3U1KTW1lY99dRT+vd//3etW7dObrdb3/72t7V7925NnTpVjY2NGjRokIYNGxbwvVlZWWpsbJQkNTY2KjMzs9vPz8zM9Lfpau3atVqzZk04X44p9FYAXDIuW8/vrWcZbpzjIM7IowAasK6whhiv1ytJuvPOO/XII49Ikm644Qbt379fzz33nKZOndrr9xqGIYfjqzeWS/+7tzaXWrVqlR599FH/1y0tLcrNze3X6zCLy51m/Pzeei24JU873mvo9YgDxFaop1EjdIREwNrCGmKGDx+upKQk5efnBzw+ZswY7du3T5Lkcrl04cIFNTc3B4zGNDU1afLkyf42n332Wbef//nnnysrK6vHvzslJUUpKcHtRmsFfR3k6JC0470G7VkxTdVHm/kUGmeC+fdbs/OgZua7+PfqJ0IiYH1hrYkZNGiQbr75Zn388ccBjx8+fFijRo2SJI0fP17JyckqLy/3P9/Q0KC6ujp/iCksLJTH41FVVZW/zYEDB+TxePxt7KZr3URlkAdFVh9tZgVSHOrvadQIDqe1A/YQ8khMa2urPv30U//X9fX1qq2tVXp6ukaOHKkVK1bonnvu0S233KJp06bJ7XZr586devPNNyVJTqdTDz74oJYtW6aMjAylp6dr+fLlGjt2rGbMmCHp4shNUVGR5s+fr02bNkmSFixYoOLi4qBWJllNT0PiVw5ODup7OcgxPnEQZ2RxWjtgDyGHmHfeeUfTpk3zf+2rQ5k3b542b96sv//7v9dzzz2ntWvXasmSJRo9erT++7//W1OmTPF/z7PPPqukpCTdfffd/s3uNm/erMTERH+bl19+WUuWLPGvYiopKel1b5p4N5DCwt6GxM+c7wjq+znIMT6F+6BPBCIkAvbAsQMRFsnjAy7Hd5DjvpW3MYUUh3z/tn0d9Mm/X/+wVxJgXjHbJwaBejuE0VdY6K5ruOz39zUk3huWUce/cB30iZ75ToO38l5J7C8EEGIiJhyFhcEOdXetj+E0Y3PgNOrIsXpIdNc1aMq6N3TfC5V6eEut7nuhUlPWvdHnByPAasK6xBpfCUdhYbD1EL/6zo1KcHCQoxkN5KBPXJ4vJHadzjX7XkksHQe+QoiJkHAUFvqGxPuqm5h0NUunzYzTqCPHaiGR/YWAQEwnRUiwoyh/Odve65SS1YfEgWjo72nw8Yj9hYBAhJgI6auw0Offfn+o17nsTq8h5+BB+udvfV3DhlD3Em4URsJsWDoOBGI6KUJ8oygLy2q6HcLYVU9z2T0tzU4fMkh33ZCjmfkuUw+JxwPO1IEZsb8QEIiRmAjqbfVJV11XK/W2NLv53AX9+v/+SZ7zFwgwAzDQpe9ArNhh6TgQCkJMhBUVZGvfytv0+B1jLtvON5ddeeQUZ75EEGfqwMyokwMCEWKiIDHBoeFDgzthu+KPf6FwL4IojITZsb8Q8BVqYqIk+Dnq4D5BUbjXPxRGwgqstnQc6C9CTJQEu+dL4TUZWr/70x5aBKJwr38ojIRVsL8QwHRS1AQ7lz3p6gwK9yKIwkgAsA5CTBQFM5dN4V5k0b8AYB0OwzAsuQwjlKO8o63Ta/Q5l80+JpFF/wJAfArl/k2IiWPBhB30H/0LAPEnlPs3hb1xjMK9yKJ/e0fAA2AGhBgAAZhqA2AWFPYC8ONIBgBmQoiBJE50BkcyADAfppPA9AEkhXYkA7VEAOIBIzE2x/QBfDiSAYDZEGJsjOkDXIojGQCYDSHGxjjRGZfiSAYAZkOIsTGmD3ApjmQAYDaEGBtj+gBdBXO+FwDEC1Yn2Zhv+qDR09ZjXYxDF29eTB/YS1FBtmbmu9ixF0DcI8TYmG/6YGFZjRxSQJBh+sDeOJIBgBkwnWQSkdqMjukDAIBZMRJjApHejI7pAwCAGRFi4pxvM7qu4y6+zegGMlrCScUAADMjxIQomjf+vjajc+jiZnQz810hXwNHDQBAZPFBMfIIMSGI9o0/UmfZRHJ0B7A6bkwIBh8Uo4MQE6RY3PgjsRldJEd3AKvjxoRg8EExelidFIRYnTEUic3oOGoA6B8OS0UwOJMuuggxQYjVjT8SZ9lw1AAQOm5MCBYfFKOLEBOEWN34I3GWDUcNAKHjxoRg8UExuggxQYjljT/cm9FxUjEQOm5MCBYfFKOLwt4gxPqMoXBuRsdRA0DouDEhWLG+X9gNIzFBiMS0Tn+uofCaDN15w1UqvCZjQH8XRw0AoWEEE8GKh/uFnTgMw7BkJVpLS4ucTqc8Ho/S0tLC8jPX/uGgXnirXpfW7iU4pPl/m6dVf5cflr8jmtjvAgieb3WS1PMIJh8AcCmW4/dfKPdvQkyQelv3L118E+MNDLA+bkwIBR8U+4cQo/CGmE6voSnr3uh1dYJvjnPfytv4BQUsjhsTEFmh3L8p7A1CpLb/B2A+vvq0WCBAAYEIMUFgeSWAWGMqC+iO1UlBYHklgFjiyAOgZ4SYILC8EkCscOQB0DtCTBDiYd1/p9dQxZFTerX2hCqOnOINC7AJjjwAekdNTJB8G8R1nZN2RWFOmrlwwL6oyQN6R4gJQTi3/w9Wb/vT+ObC2Z8GsDZq8oDeEWJCFM3llX3NhTt0cS58Zr6LZZaARXEWD9A7amLiGHPhAOKhJg+IV4SYOMZcOACJQ1uB3jCdFMeYC48sdj+FmcSiJg+Id4SYOGbmufB4Dwis+IIZxfLIAyAeEWLimG8ufGFZjRxSQJCJ57nweA8IrPgCAGugJibOmW0uPN63R2f3UwCwDkZiTMAsc+FmWBLOieQAYB2EGJMww1y4GQICK74AwDqYTkLYmCEgsOILAKyDEIOwMUNA4ERyALAOQgzCxgwBgd1PAcA6CDEIG7MEBLOt+AIA9MxhGIYl15K2tLTI6XTK4/EoLS0t1pdjK/G0T8zlNt2L9w35AMCOQrl/E2IQEfEQEOIpTAEAghPK/Tvk6aS9e/dqzpw5ysnJkcPh0Pbt23tt+93vflcOh0M/+9nPAh5vb2/X4sWLNXz4cA0ZMkQlJSU6fvx4QJvm5maVlpbK6XTK6XSqtLRUZ86cCfVyESO+JeF33nCVCq/JiEmAiedN9wAAAxdyiDl37pzGjRun9evXX7bd9u3bdeDAAeXk5HR7bunSpdq2bZu2bNmiffv2qbW1VcXFxers7PS3mTt3rmpra+V2u+V2u1VbW6vS0tJQLxc2xK68AGAPIW92d/vtt+v222+/bJsTJ05o0aJFeu2113THHXcEPOfxePTiiy/qpZde0owZMyRJZWVlys3N1a5duzR79mwdOnRIbrdblZWVmjhxoiTphRdeUGFhoT7++GONHj061MuGjZhh0z2EVzxMXwKIvrDv2Ov1elVaWqoVK1bo+uuv7/Z8dXW1Ojo6NGvWLP9jOTk5Kigo0P79+zV79mxVVFTI6XT6A4wkTZo0SU6nU/v37+8xxLS3t6u9vd3/dUtLS5hfGczCDJvuIXyofQLsK+xLrNetW6ekpCQtWbKkx+cbGxs1aNAgDRs2LODxrKwsNTY2+ttkZmZ2+97MzEx/m67Wrl3rr59xOp3Kzc0d4CuBWZlh0z2EB7VPgL2FNcRUV1fr5z//uTZv3iyHI7ShXMMwAr6np+/v2uZSq1atksfj8f85duxYaBcPyzDDpnsYOGqfAIQ1xLz11ltqamrSyJEjlZSUpKSkJB09elTLli3T17/+dUmSy+XShQsX1NzcHPC9TU1NysrK8rf57LPPuv38zz//3N+mq5SUFKWlpQX8gT2ZZdM9DEwotU8ArCmsIaa0tFTvv/++amtr/X9ycnK0YsUKvfbaa5Kk8ePHKzk5WeXl5f7va2hoUF1dnSZPnixJKiwslMfjUVVVlb/NgQMH5PF4/G2Ay2FXXmvq9BqqOHJKr9ae0P/99POgvofap8B+qzhyitEpWEbIhb2tra369NNP/V/X19ertrZW6enpGjlypDIyAld7JCcny+Vy+YtxnU6nHnzwQS1btkwZGRlKT0/X8uXLNXbsWP9qpTFjxqioqEjz58/Xpk2bJEkLFixQcXExK5MQtKKCbM3Md7FqxSJ6KuANht1rnyh8hpWFHGLeeecdTZs2zf/1o48+KkmaN2+eNm/eHNTPePbZZ5WUlKS7775b58+f1/Tp07V582YlJib627z88stasmSJfxVTSUlJn3vTAF35Nt2DufkKeEMZP3Do4sibnWufeus3X+Ezo5IwO44dABDXOr2Gpqx7I6QRGN9Ym51v0n31my/k7Vt5G6OTiCsRPXYAAMKpr3qNvgp4e0LtE4XPsIewb3YHAMEKpl4j2MLcRdP+Wtdm/RW1T/8fmz7CDhiJARATwW5UF2xh7rf+enjMDhyNR2z6CDsgxACIulA2qjPz5oWxXNps5n4DgsV0EoCoC/WQztVz8rWwrEYOKSD4xPPmhbFe2uzb9NFs/QaEgpEYAFEXar2G2TYvjJcznczWb0CoGIkBEHX9qdcwy+aFfU2VOXRxqmxmvisq126WfgP6gxADIOp89RqNnrYeb/a9bVRnhs0LQ50qiwYz9BvQH0wnAYg6Kx/SydJmIHoIMQBiwqr1GixtBqKH6SQAMWPFeo3+TpUBCB0hBkBMWa1eg6XNQPQwnQQAYWbVqTIg3jASAwARYMWpMiDeEGIAIEKsNlUGxBumkwAAgCkRYgAAgCkRYgAAgCkRYgAAgClR2AsAQAR0eg1Wp0UYIQYAgDBz1zVozc6DAYeBZjtTtXpOPvsEhRHTSQAAhJG7rkELy2q6nWbe6GnTwrIauesaYnRl1kOIASDp4tB3xZFTerX2hCqOnFKnt6eTfwBcTqfX0JqdB3s8N8v32JqdB/n/K0yYTgLA0LeoX0B4VNWf7jYCcylDUoOnTVX1p9kIMQwIMYDN+Ya+u34u9A192+GsH0IcwqXpbO8Bpj/tcHlMJwE2xtA39QsIr8yhqX03CqEdLo8QA9hYKEPfVkSIQ7hNyEtXtjNVvU1EOnRxlG9CXno0L8uyCDGAjdl96NvuIQ7hl5jg0Oo5+ZLULcj4vl49J596qzAhxAA2Zvehb7uHOERGUUG2Nt5/o1zOwP9vXM5UW9SYRROFvYCN+Ya+Gz1tPU6pOHTxjdeqQ9/D/yolrO0An6KCbM3Md7HiLcIIMYCN+Ya+F5bVyCEFBBlbDH0HW+pCSQz6ITHBwTLqCGM6CbA5Ow99/+Vce1jbAYguRmIA2Hbo2+41QYDZEWIASLLn0Lfda4IAs2M6CYBtsRwWMDdCDABbs3NNEGB2TCcBsD271gQBZkeIAQDZsyYIMDumkwAAgCkRYgAAgCkRYgAAgClREwNJUqfXoKgRAGzASu/3hBjIXdegNTsPqsHz1Um92c5UrZ6Tz/LSKLPSmwuA+GO193uHYRiWPNqspaVFTqdTHo9HaWlpsb6cuOWua9DCsppuu5X6bpvskxE9VntzQfwjNNuLWd7vQ7l/E2JsrNNraMq6NwJumpfybbm+b+VtvLFFmFneXGAdhGZ7MdP7fSj3bwp7bayq/nSvv9CSZEhq8LSpqv509C7Khjq9htbsPNjj2T2+x9bsPKhOryU/byAGfKG56///jZ42LSyrkbuuIUZXhkix6vs9IcbGms72/gvdn3boH6u+uSA+xUto7vQaqjhySq/WnlDFkVOE9Aiz6vs9hb02ljk0te9GIbRD/1j1zQXxKZTQHKkdjJnKij6rvt8zEmNjE/LSle1M7XZ6r49DF99YJuSlR/OybMeqby6IT7EOzUxlxYZV3+8JMTaWmODQ6jn5ktTtF9v39eo5+TEv8rI6q765ID7FMjTHy1SWHVn1/Z4QY3NFBdnaeP+NcjkD37BczlRWxESJVd9cEJ/CHZpDqW2h/iu2rPh+T00MVFSQrZn5LvaLiCHfm0vXOgEXdQIIM19oXlhWI4cUMCoSamgOtbYl1lNZsN77PfvEAHGEzccQLQMtru3P3kYVR07pvhcq+/zZv5k/KWJFxYh/ody/GYkB4khigoM3b0TFQD6R91Xb4tDF2paZ+a6An+ebymr0tPX4vb4N16j/QrCoiQEAm/KF5jtvuEqF12QEPerX39oW6r8QboQYAEBIBlLbYsXiUsQO00kAgJAMdJm21YpLETuEGABASMJR20L9F8KB6SQAQEiobUG8IMQAAEJGbQviAdNJAIB+obYFsUaIAQD0G7UtiCWmkwAAgCkRYgAAgCmFHGL27t2rOXPmKCcnRw6HQ9u3b/c/19HRoZUrV2rs2LEaMmSIcnJy9MADD+jkyZMBP6O9vV2LFy/W8OHDNWTIEJWUlOj48eMBbZqbm1VaWiqn0ymn06nS0lKdOXOmXy8SAABYT8gh5ty5cxo3bpzWr1/f7bkvvvhCNTU1evzxx1VTU6OtW7fq8OHDKikpCWi3dOlSbdu2TVu2bNG+ffvU2tqq4uJidXZ2+tvMnTtXtbW1crvdcrvdqq2tVWlpaT9eIgAAsKIBnWLtcDi0bds23XXXXb22efvttzVhwgQdPXpUI0eOlMfj0de+9jW99NJLuueeeyRJJ0+eVG5urv7whz9o9uzZOnTokPLz81VZWamJEydKkiorK1VYWKiPPvpIo0eP7vPaOMUaAADzCeX+HfGaGI/HI4fDoSuvvFKSVF1drY6ODs2aNcvfJicnRwUFBdq/f78kqaKiQk6n0x9gJGnSpElyOp3+Nl21t7erpaUl4A8AALCuiIaYtrY2PfbYY5o7d64/TTU2NmrQoEEaNmxYQNusrCw1Njb622RmZnb7eZmZmf42Xa1du9ZfP+N0OpWbmxvmVwMAAOJJxEJMR0eH7r33Xnm9Xm3YsKHP9oZhyOH4aoOkS/+7tzaXWrVqlTwej//PsWPH+n/xAAAg7kUkxHR0dOjuu+9WfX29ysvLA+a0XC6XLly4oObm5oDvaWpqUlZWlr/NZ5991u3nfv755/42XaWkpCgtLS3gDwAAsK6whxhfgPnkk0+0a9cuZWQE7uQ4fvx4JScnq7y83P9YQ0OD6urqNHnyZElSYWGhPB6Pqqqq/G0OHDggj8fjbwMAAOwt5GMHWltb9emnn/q/rq+vV21trdLT05WTk6N/+Id/UE1NjX73u9+ps7PTX8OSnp6uQYMGyel06sEHH9SyZcuUkZGh9PR0LV++XGPHjtWMGTMkSWPGjFFRUZHmz5+vTZs2SZIWLFig4uLioFYmAQAA6wt5ifWbb76padOmdXt83rx5euKJJ5SXl9fj9+3evVu33nqrpIsFvytWrNArr7yi8+fPa/r06dqwYUNAMe7p06e1ZMkS7dixQ5JUUlKi9evX+1c59YUl1gBgTp1eg0MlbSyU+/eA9omJZ4QYADAfd12D1uw8qAZPm/+xbGeqVs/JV1FBdgyvDNESV/vEAAAQDHddgxaW1QQEGElq9LRpYVmN3HUNMboyxCtCDAAg5jq9htbsPKiepgZ8j63ZeVCdXktOHqCfCDEAgJirqj/dbQTmUoakBk+bqupPR++iEPcIMQCAmGs623uA6U872AMhBgAQc5lDU8PaDvZAiAEAxNyEvHRlO1PV20Jqhy6uUpqQlx7Ny0KcI8QAAGIuMcGh1XPyJalbkPF9vXpOPvvFIAAhBgAQF4oKsrXx/hvlcgZOGbmcqdp4/43sE4NuQj52AACASCkqyNbMfBc79iIohBgAQFxJTHCo8JqMvhvC9phOAgAApkSIAQAApkSIAQAApkSIAQAApkSIAQAApkSIAQAApkSIAQAApkSIAQAApkSIAQAApkSIAQAApkSIAQAApkSIAQAApkSIAQAApkSIAQAApkSIAQAApkSIAQAApkSIAQAAppQU6wsAAADm0uk1VFV/Wk1n25Q5NFUT8tKVmOCI+nUQYgAAQNDcdQ1as/OgGjxt/seynalaPSdfRQXZUb0WppMAAEBQ3HUNWlhWExBgJKnR06aFZTVy1zVE9XoIMQAAoE+dXkNrdh6U0cNzvsfW7DyoTm9PLSKDEAMAAPpUVX+62wjMpQxJDZ42VdWfjto1EWIAAECfms72HmD60y4cCDEAAKBPmUNTw9ouHAgxAACgTxPy0pXtTFVvC6kdurhKaUJeetSuiRADAAD6lJjg0Oo5+ZLULcj4vl49Jz+q+8UQYgAAQFCKCrK18f4b5XIGThm5nKnaeP+NUd8nhs3uAABA0IoKsjUz38WOvQAAwHwSExwqvCYj1pfBdBIAADAnQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAly+7YaxiGJKmlpSXGVwIAAILlu2/77uOXY9kQc/bsWUlSbm5ujK8EAACE6uzZs3I6nZdt4zCCiTom5PV6dfLkSQ0dOlQOx1eHUrW0tCg3N1fHjh1TWlpaDK8wPtAfX6EvAtEfgeiPQPRHIPrjKwPtC8MwdPbsWeXk5Cgh4fJVL5YdiUlISNCIESN6fT4tLc32v2iXoj++Ql8Eoj8C0R+B6I9A9MdXBtIXfY3A+FDYCwAATIkQAwAATMl2ISYlJUWrV69WSkpKrC8lLtAfX6EvAtEfgeiPQPRHIPrjK9HsC8sW9gIAAGuz3UgMAACwBkIMAAAwJUIMAAAwJUIMAAAwJVuFmA0bNigvL0+pqakaP3683nrrrVhfUlSsXbtWN998s4YOHarMzEzddddd+vjjjwPaGIahJ554Qjk5ORo8eLBuvfVWffjhhzG64uhZu3atHA6Hli5d6n/Mbn1x4sQJ3X///crIyNAVV1yhG264QdXV1f7n7dQfX375pf7lX/5FeXl5Gjx4sK6++mr967/+q7xer7+Nlftj7969mjNnjnJycuRwOLR9+/aA54N57e3t7Vq8eLGGDx+uIUOGqKSkRMePH4/iqwify/VHR0eHVq5cqbFjx2rIkCHKycnRAw88oJMnTwb8DLv0R1ff/e535XA49LOf/Szg8XD3h21CzG9/+1stXbpUP/rRj/Tuu+/qb//2b3X77bfrz3/+c6wvLeL27Nmjhx56SJWVlSovL9eXX36pWbNm6dy5c/42Tz/9tJ555hmtX79eb7/9tlwul2bOnOk/g8qK3n77bT3//PP6xje+EfC4nfqiublZ3/rWt5ScnKz/+Z//0cGDB/XTn/5UV155pb+Nnfpj3bp1eu6557R+/XodOnRITz/9tH7yk5/ol7/8pb+Nlfvj3LlzGjdunNavX9/j88G89qVLl2rbtm3asmWL9u3bp9bWVhUXF6uzszNaLyNsLtcfX3zxhWpqavT444+rpqZGW7du1eHDh1VSUhLQzi79cant27frwIEDysnJ6fZc2PvDsIkJEyYY3/ve9wIeu+6664zHHnssRlcUO01NTYYkY8+ePYZhGIbX6zVcLpfx1FNP+du0tbUZTqfTeO6552J1mRF19uxZ49prrzXKy8uNqVOnGg8//LBhGPbri5UrVxpTpkzp9Xm79ccdd9xh/PM//3PAY9/+9reN+++/3zAMe/WHJGPbtm3+r4N57WfOnDGSk5ONLVu2+NucOHHCSEhIMNxud9SuPRK69kdPqqqqDEnG0aNHDcOwZ38cP37cuOqqq4y6ujpj1KhRxrPPPut/LhL9YYuRmAsXLqi6ulqzZs0KeHzWrFnav39/jK4qdjwejyQpPT1dklRfX6/GxsaA/klJSdHUqVMt2z8PPfSQ7rjjDs2YMSPgcbv1xY4dO3TTTTfpH//xH5WZmalvfvObeuGFF/zP260/pkyZov/93//V4cOHJUnvvfee9u3bp7/7u7+TZL/+uFQwr726ulodHR0BbXJyclRQUGD5/pEuvrc6HA7/SKbd+sPr9aq0tFQrVqzQ9ddf3+35SPSHZQ+AvNRf/vIXdXZ2KisrK+DxrKwsNTY2xuiqYsMwDD366KOaMmWKCgoKJMnfBz31z9GjR6N+jZG2ZcsW1dTU6O233+72nN364o9//KM2btyoRx99VD/84Q9VVVWlJUuWKCUlRQ888IDt+mPlypXyeDy67rrrlJiYqM7OTv34xz/WfffdJ8l+vx+XCua1NzY2atCgQRo2bFi3NlZ/r21ra9Njjz2muXPn+g89tFt/rFu3TklJSVqyZEmPz0eiP2wRYnwcDkfA14ZhdHvM6hYtWqT3339f+/bt6/acHfrn2LFjevjhh/X6668rNTW113Z26Avp4ienm266SU8++aQk6Zvf/KY+/PBDbdy4UQ888IC/nV3647e//a3Kysr0yiuv6Prrr1dtba2WLl2qnJwczZs3z9/OLv3Rk/68dqv3T0dHh+699155vV5t2LChz/ZW7I/q6mr9/Oc/V01NTcivbSD9YYvppOHDhysxMbFb0mtqaur2qcLKFi9erB07dmj37t0aMWKE/3GXyyVJtuif6upqNTU1afz48UpKSlJSUpL27NmjX/ziF0pKSvK/Xjv0hSRlZ2crPz8/4LExY8b4C97t9LshSStWrNBjjz2me++9V2PHjlVpaakeeeQRrV27VpL9+uNSwbx2l8ulCxcuqLm5udc2VtPR0aG7775b9fX1Ki8v94/CSPbqj7feektNTU0aOXKk/7316NGjWrZsmb7+9a9Likx/2CLEDBo0SOPHj1d5eXnA4+Xl5Zo8eXKMrip6DMPQokWLtHXrVr3xxhvKy8sLeD4vL08ulyugfy5cuKA9e/ZYrn+mT5+uDz74QLW1tf4/N910k77zne+otrZWV199tW36QpK+9a1vdVtuf/jwYY0aNUqSvX43pIsrThISAt8WExMT/Uus7dYflwrmtY8fP17JyckBbRoaGlRXV2fJ/vEFmE8++US7du1SRkZGwPN26o/S0lK9//77Ae+tOTk5WrFihV577TVJEeqPfpUDm9CWLVuM5ORk48UXXzQOHjxoLF261BgyZIjxpz/9KdaXFnELFy40nE6n8eabbxoNDQ3+P1988YW/zVNPPWU4nU5j69atxgcffGDcd999RnZ2ttHS0hLDK4+OS1cnGYa9+qKqqspISkoyfvzjHxuffPKJ8fLLLxtXXHGFUVZW5m9jp/6YN2+ecdVVVxm/+93vjPr6emPr1q3G8OHDjR/84Af+Nlbuj7Nnzxrvvvuu8e677xqSjGeeecZ49913/attgnnt3/ve94wRI0YYu3btMmpqaozbbrvNGDdunPHll1/G6mX12+X6o6OjwygpKTFGjBhh1NbWBry3tre3+3+GXfqjJ11XJxlG+PvDNiHGMAzjV7/6lTFq1Chj0KBBxo033uhfYmx1knr88+tf/9rfxuv1GqtXrzZcLpeRkpJi3HLLLcYHH3wQu4uOoq4hxm59sXPnTqOgoMBISUkxrrvuOuP5558PeN5O/dHS0mI8/PDDxsiRI43U1FTj6quvNn70ox8F3JSs3B+7d+/u8b1i3rx5hmEE99rPnz9vLFq0yEhPTzcGDx5sFBcXG3/+859j8GoG7nL9UV9f3+t76+7du/0/wy790ZOeQky4+8NhGIbRvzEcAACA2LFFTQwAALAeQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADCl/weVdtX+wf1bmQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_correaltion(df.hardness, df.mortality)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x146a41a10>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# модель линейно регрессии\n",
    "X = df.hardness.values.reshape(-1, 1)\n",
    "y = df.mortality.values\n",
    "\n",
    "# Создание и обучение модели\n",
    "model = LinearRegression()\n",
    "model.fit(X, y)\n",
    "\n",
    "# Предсказание на том же диапазоне значений, что и обучающие данные\n",
    "X_range = np.linspace(min(X), max(X), 100).reshape(-1, 1)\n",
    "y_pred = model.predict(X_range)\n",
    "\n",
    "plt.scatter(X, y)\n",
    "plt.plot(X_range, model.predict(X_range), color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Коэффициент детерминации (R^2): 0.4288267193124974\n"
     ]
    }
   ],
   "source": [
    "# Рассчет коэффициента детерминации\n",
    "# Предсказанные значения\n",
    "y_pred = model.predict(X)\n",
    "\n",
    "# Рассчитываем R^2\n",
    "r2 = r2_score(y, y_pred)\n",
    "print(f\"Коэффициент детерминации (R^2): {r2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Вывод графика остатков\n",
    "\n",
    "# Рассчитываем остатки\n",
    "residuals = y - y_pred\n",
    "\n",
    "# Построение графика остатков\n",
    "plt.scatter(X, residuals)\n",
    "plt.axhline(y=0, color='r', linestyle='--')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task#2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_south = df.loc[df.location == 'South',:]\n",
    "df_north = df.loc[df.location == 'North',:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Корреляция Спирмена имеет статистическую значимость ( p < 0.15) и равна -0.4042078956511175\n",
      "Корреляция Пирсона имеет статистическую значимость ( p < 0.15) и равна -0.36859783832887194\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_correaltion(df_north.hardness, df_north.mortality)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14638abd0>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# модель линейно регрессии\n",
    "X = df_north.hardness.values.reshape(-1, 1)\n",
    "y = df_north.mortality.values\n",
    "\n",
    "# Создание и обучение модели\n",
    "model = LinearRegression()\n",
    "model.fit(X, y)\n",
    "\n",
    "# Предсказание на том же диапазоне значений, что и обучающие данные\n",
    "X_range = np.linspace(min(X), max(X), 100).reshape(-1, 1)\n",
    "y_pred = model.predict(X_range)\n",
    "\n",
    "plt.scatter(X, y)\n",
    "plt.plot(X_range, model.predict(X_range), color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Коэффициент детерминации (R^2): 0.1358643664207173\n"
     ]
    }
   ],
   "source": [
    "# Рассчет коэффициента детерминации\n",
    "# Предсказанные значения\n",
    "y_pred = model.predict(X)\n",
    "\n",
    "# Рассчитываем R^2\n",
    "r2 = r2_score(y, y_pred)\n",
    "print(f\"Коэффициент детерминации (R^2): {r2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Коэффициент детерминации очень низкий указывает на отсутствие объяснённой дисперсии"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Вывод графика остатков\n",
    "\n",
    "# Рассчитываем остатки\n",
    "residuals = y - y_pred\n",
    "\n",
    "# Построение графика остатков\n",
    "plt.scatter(X, residuals)\n",
    "plt.axhline(y=0, color='r', linestyle='--')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Корреляция Спирмена имеет статистическую значимость ( p < 0.15) и равна -0.5957229185013566\n",
      "Корреляция Пирсона имеет статистическую значимость ( p < 0.15) и равна -0.6021532715484156\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_correaltion(df_south.hardness, df_south.mortality)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x146f04bd0>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# модель линейно регрессии\n",
    "X = df_south.hardness.values.reshape(-1, 1)\n",
    "y = df_south.mortality.values\n",
    "\n",
    "# Создание и обучение модели\n",
    "model = LinearRegression()\n",
    "model.fit(X, y)\n",
    "\n",
    "# Предсказание на том же диапазоне значений, что и обучающие данные\n",
    "X_range = np.linspace(min(X), max(X), 100).reshape(-1, 1)\n",
    "y_pred = model.predict(X_range)\n",
    "\n",
    "plt.scatter(X, y)\n",
    "plt.plot(X_range, model.predict(X_range), color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Коэффициент детерминации (R^2): 0.3625885624364602\n"
     ]
    }
   ],
   "source": [
    "# Рассчет коэффициента детерминации\n",
    "# Предсказанные значения\n",
    "y_pred = model.predict(X)\n",
    "\n",
    "# Рассчитываем R^2\n",
    "r2 = r2_score(y, y_pred)\n",
    "print(f\"Коэффициент детерминации (R^2): {r2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Коэффициент детерминации очень низкий указывает на отсутствие объяснённой дисперсии"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Вывод графика остатков\n",
    "\n",
    "# Рассчитываем остатки\n",
    "residuals = y - y_pred\n",
    "\n",
    "# Построение графика остатков\n",
    "plt.scatter(X, residuals)\n",
    "plt.axhline(y=0, color='r', linestyle='--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вывод: небольшая зависимость есть, график и тесты по всем регионам показывает наличие этой связи. Анализ по регионам почти не показал наличие зависимости.  Теоретически можно сказать что с увеличением жесткости воды смертность уменьшается"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
